TS-Inverse
A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models
Caspar Meijer (TNO)
Jiyue Huang (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Shreshtha Sharma (TNO)
Elena Lazovik (TNO)
Lydia Y. Chen (TU Delft - Electrical Engineering, Mathematics and Computer Science, University of Neuchâtel)
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Abstract
Federated learning (FL) for time series forecasting (TSF) enables clients with privacy-sensitive time series (TS) data to collaboratively learn accurate forecasting models, for example, in energy load prediction. Unfortunately, privacy risks in FL persist, as servers can potentially reconstruct clients' training data through gradient inversion attacks (GIA). Although GIA is demonstrated for image classification tasks, little is known about time series regression tasks. In this paper, we first conduct an extensive empirical study on inverting TS data across 4 TSF models and 4 datasets, identifying the unique challenges of reconstructing both observations and targets of TS data. We then propose TS-Inverse, a novel GIA that improves the inversion of TS data by (i) learning a gradient inversion model that outputs quantile predictions, (ii) a unique loss function that incorporates periodicity and trend regularization, and (iii) regularization according to the quantile predictions. Our evaluations demonstrate a remarkable performance of TS-Inverse, achieving at least a 2x-10x improvement in terms of the sMAPE metric over existing GIA methods on TS data. Code repository: https://github.com/Capsar/ts-inverse